Identifying economics’ place amongst academicdisciplines: a science or a social science?

John Hudson1

Received: 7 December 2016 / Published online: 13 September 2017� The Author(s) 2017. This article is an open access publication

Abstract Different academic disciplines exhibit different styles, including styles in

journal titles. Using data from the 2014 Research Excellence Framework (REF) in the UK

we are able to identify the stylistic trends of different disciplines using 155,552 journal

titles across all disciplines. Cluster analysis is then used to group the different disciplines

together. The resulting identification fits the social sciences, the sciences and the arts and

humanities reasonably well. Economics overall, fits best with philosophy, but the linkage is

weak. When we divided economics into papers published in theory, econometrics and the

remaining journals, the first two link with mathematics and computer science, particularly

econometrics, and thence the sciences. The rest of economics then links with business and

thence the social sciences.

Keywords Economics � Journal titles � REF � Cluster analysis

Introduction

Is economics a science or a social science? Arguments have been made for both orien-

tations, i.e. that economics is a social science (Frey 1999), a science (Frey 1999) and even a

moral science (Schabas 2009). In terms of sciences it has been linked with physics, with

many physicists doubling as, or transforming into, economists and a subpart of physics,

econophysics, specifically evolving which deals with economics (Stanley et al. 1999).

There are also links with biology (Marshall 1920; Daly 1968). Frey (1999) argues eco-

nomics is a social science as it is part of those sciences which deal with actual problems of

society, i.e. it is the subject matter which makes it a social science. However, he also points

out that in practice economists tend to fill the journals with axioms, lemmas and proofs, i.e.

they adopt what they perceive as a scientific, and particularly a mathematical,

& John HudsonJ.R.Hudson@bath.ac.uk

1 Department of Economics, University of Bath, Bath BA2 7AY, UK

123

Scientometrics (2017) 113:735–750DOI 10.1007/s11192-017-2519-3

methodology. Mayer (1980) argues that economists act as though economics is a hard

science and this is reflected by their sophisticated use of mathematics. However, he then

goes on to argue that econometrics is not sufficiently advanced to enable us to test theories

as a hard scientist would. With respect to this argument, it is worth noting that econo-

metrics has moved a long way since 1980, when Mayer wrote his paper and perhaps that

objection is not as valid today as it was then. In any case, part of the reason for this

disagreement as to the nature of economics may be because the subject matter of eco-

nomics deals with people, whether as agents in firms or consumers, either as individuals or

aggregates. This will tend to place it as a social science. But in terms of methodology it has

arguably closer links with the sciences. It is essentially a quantitative discipline. Theo-

retical models are built to replicate and simplify some real world situation. Empirical work

is focused on econometrics and many of the techniques used and developed by economists

are also used outside the discipline and often in the sciences, such as biological sciences.

Economics has of course evolved over the years. Fourcade et al. (2015) argues that for

much of the period since 1945, a theoretical approach based on rigorous mathematical

modelling was the main path to establishing scientific purity in economics. However, in the

1990s and 2000s there occurred a shift in emphasis from theory to empirical work. Einav

and Levin (2014) potentially link this shift to a substantial increase in data availability.

They argue that this may change the emphasis on setting up and testing hypotheses to one

of searching for the best possible explanation. It may also further move economics away

from the emphasis on theory, all of which may in turn impact on how economists perceive

themselves and the relationship of economics with other disciplines. The quantitative

nature of most economics research is in contrast to the qualitative methods that charac-

terize the work of many other social scientists. Mankiw (2006) epitomises this perception,

when he admitted he thought of himself as, and sought to project the image of, a scientist.

He admitted that this might amuse physics academics, but he would remind them that

economists formulate theories with mathematical precision, collect huge data sets1 on

individual and aggregate behaviour, and exploit the most sophisticated statistical tech-

niques to reach empirical judgments that are, potentially at least, free of bias and ideology.

However he also thought that economists could be likened to engineers, having been put on

earth not to propose and test elegant theories, but to solve practical problems. This per-

ception that economics is different from the social sciences may also have contributed to

the insularity of the discipline, as reflected for example in the relative dislike of economists

for interdisciplinary work when contrasted with other social scientists (Fourcade et al.

2015), as well as a reluctance to cite work from these other disciplines (Romer 2015;

Pieters and Baumgartner 2002).

There have been a number of attempts to map academic disciplines on the basis of

citation or similar data. It is often done for specific disciplines, rather than science as a

whole. Thus McCain (1991) analyses co-citation data,2 using cluster analysis and multi-

dimensional scaling, to produce maps of highly cited and co-cited documents in clusters

representing interrelated research activities. The focus is on mapping economics journals,

and is able to identify several major groups comprising: the ‘core of economics’, a West

European set of journals, formal journals, basically key theory and econometrics journals,

1 Of course Mankiw’s statement needs to be qualified, compared with meteorological research, for example,economists’ data sets are relatively small.2 Co-citation data relates to the number of times two ‘works’, broadly defined, are jointly cited. This can betwo specific papers or journals. Alternatively, author co-citation focuses on the number of times any twoauthors are cited together.

736 Scientometrics (2017) 113:735–750

123

public finance journals and economics journals.3 A number of studies do attempt to map

the full range of disciplines. Rafols et al. (2010) using a matrix of similarity measures

based on correlations of different pieces of information items, conclude that science is a

fragmentary structure composed of both solid clusters and empty spaces. This gives sup-

port to the concept of independent, but potentially linked disciplines, rather than a con-

tinuum of study with somewhat artificial boundaries. The analysis is based on citing

similarities among ISI subject data. There is an important biomedical research cluster and a

major physical sciences cluster, comprising engineering, physics and material sciences.

There is a rather diffuse social sciences cluster, which Rafols et al. argue is due to

relatively low citation rates. Economics is part of this cluster, but with some links to

mathematics and computer science. Bollen et al. (2009) use click stream data to construct a

journal network that outlines the relationships among various scientific domains. Eco-

nomics is on the periphery of the social sciences cluster. But it is also one side of a bridge

connecting the social sciences to physics and engineering via production and

manufacturing.

Hence these mappings tend to loosely tie economics to the social sciences, but often

with some linkage to parts of the sciences. However most of these analyses are based on

citations or similar measures, rather than the similarities in the structure of academic work.

We have already emphasised that economics analyses social science issues, but that it is

the methodology or the style of working which differentiates it from the social sciences and

links it to the sciences. It is almost inevitable that economics’ citations will be greater in

social sciences, than the sciences. But this tells us relatively little about the similarities

between styles of working. There is little hard evidence on subject linkages based on

different styles and in order to provide the beginnings of a perspective on this, we compare

the journal title characteristics of the different disciplines. The characteristics we will focus

on include title length, average word length, the proportion of papers containing a colon,

the proportion containing a question mark, the average publication date and the number of

authors. In particular we will extract the economics’ footprint as reflected in these title

characteristics. We will then compare this footprint with those of other disciplines. We will

argue that each discipline tends to adopt a certain style in terms of these characteristics.

Lewison and Hartley (2005) argue, for example, that there are substantial differences in

title length, use of colons and question marks, etc. between disciplines. Nagano (2015) also

emphasises different disciplinary conventions with respect to the title, distinguishing

between the hard (e.g. medicine and engineering) and soft (e.g. the social sciences,

including economics) sciences. Different disciplines also tend to differ in the number of

authors and the extent to which journal papers are important compared to, for example,

books. If disciplines have their own style, then it seems reasonable to assume that there are

also similarities between contiguous disciplines. For example, we would expect, as sug-

gested in the literature, the characteristics of a social science discipline to be more closely

aligned with other social science disciplines than ones in the sciences. Following on from

this, we will then use cluster analysis to place the different disciplines within groups to see

how well they fit into the social science, science and art and humanities categories. In the

process we will also identify the group and subjects to which economics most closely

belongs.

3 There is potentially a degree of overlap between these clusters. Thus Review of Economic Studies isincluded in the formal journals and the Economic Journal in the West European ones. Intuitively both couldhave been categorised as part of the core.

Scientometrics (2017) 113:735–750 737

123

In doing this analysis we make use of a unique data base obtained from the Research

Excellence Framework4 (REF) of 2014. This is the latest in a series of exercises seeking to

evaluate the quality of research done in UK universities across a range of subject areas –

termed units of assessment (UoAs). In this REF there were 36 such UoAs, as listed in

Table 1, including one for economics and econometrics.5 Table 1 also gives the number of

papers on which our analysis is based. In economics there were 2600 individual pieces of

work submitted. Not all were journal papers and a proportion were submitted more than

one once to the economics panel, i.e. by definition they were jointly authored papers. This

left 2246 separate papers to analyse. Each entry to a UoA was scrutinised by a sub-panel

working within the framework of 4 main panels—A–D. Economics was in Panel C along

with the other social sciences. The main scrutiny was on the quality of academic research

contributions, primarily journal articles and books, which were individually looked at and

assessed by panel members. Each participant (faculty member) in the REF could submit up

to four research outputs (e.g. papers). Each submission was also accompanied by a research

environment statement and a number of case studies which highlighted the impact of the

university’s research on society or the economy, widely defined (Khazragui and Hudson

2015). The evaluation thus comprised three components: (1) the quality of the four

research outputs (e.g., papers) submitted by individuals, (2) the research environment

statement and (3) the impact of the research on society or the economy. Our focus is on the

publications. Hence, and this is an important prerequisite for the study, the definition of the

discipline of the sciences and social sciences is based on the REF’s subject-classification

system. In the next section we present the summary data on title characteristics. We then

use cluster analysis to group the different disciplines together and finally conclude the

paper.

The differing characteristics of disciplines in terms of journal titles

Table 1 shows the characteristics of the titles, i.e. median title length, median word length,

proportion of papers containing a colon or a question mark, the average date of submitted

papers, etc. These calculations are done for the main subject groupings. The data relate to

journal titles only and these form the basis for the cluster analysis.6 Focusing on journal

papers, the penultimate two columns show that in economics 8.05% of papers were also

submitted in other disciplines, whilst 6.38% of papers were submitted at least twice within

the economics UoA. In the latter case they would most likely have been submitted by co-

authors in different institutions. Multiple submissions of the same paper across disciplines

were particularly common in the health and engineering UoAs. They were much less

common in the arts and humanities. Multiple submissions within a discipline were rela-

tively common in the sciences, particularly physics, and also business, but less common in

engineering UoAs. Apart from the data on multiple submissions, the other columns are

calculated on papers rather than submissions. Hence if a paper was submitted more than

once to the economics panel, it would still only be counted once in calculating the statistics

4 http://www.ref.ac.uk/.5 These are abbreviated titles. The full titles are given later in Table 2.6 This is similar to Table 2 in Hudson (2016). However in that paper the data relate to all UoA entries,whilst the data presented in this paper exclude multiple submissions within a discipline, i.e. papers arecounted just once, even if entered several times. This is because the purpose of this analysis is to focus ondifferences between disciplines whilst in the earlier paper the focus was on differences between REF entries.However, the regression analyses in that paper did exclude multiple submissions. In addition, for title length,word length and the number of authors we now use the median rather than the mean.

738 Scientometrics (2017) 113:735–750

123

Table

1Characteristicsofjournal

paper

titles

inthe2014UK

REF

Length

Authors

Date

Colon(%

)?(%

)Papers(%

)Word

length

%in

UoA

%outUoA

Number

ofpapers

Clinical

Medicine

109

92010.47

19.49

1.107

99.89

7.64

8.92

14.98

12,193

PublicHealth

112

72010.58

58.12

6.278

99.64

7.33

9.64

20.64

4396

AlliedHealth

106

52010.58

32.03

4.073

99.03

7.5

3.81

15.81

9870

Psychology

94

42010.52

36.22

5.675

99.54

7.53

9.07

10.27

8264

Biological

Sciences

97

62010.45

11.48

1.309

99.67

7.6

5.63

17.07

8098

Agriculture

107

62010.56

18.64

2.319

99.21

7.4

3.55

13.32

3751

Earth

System

s94

52010.36

26.53

3.749

99.02

7.33

6.65

10.88

4855

Chem

istry

95

52010.4

22.7

1.198

99.82

8.09

5.69

11.54

4423

Physics

77

62010.2

14.73

1.522

99.05

7.36

10.53

6.48

5718

Maths

67

22010.63

9.35

0.685

96.14

7.57

4.6

3.87

6426

ComputerScience

71

32010.59

18.4

1.015

78.55

7.78

5.19

6.15

5717

AeronauticalEng

89

42010.47

13.39

0.812

99.29

7.56

1.24

10.18

4063

ElectricalEng

86

42010.44

10.25

0.51

99.17

7.81

1.78

10.87

3921

CivilEng

85

32010.58

19.31

2.179

97.65

7.42

1.55

11.69

1331

General

Eng

87

42010.48

13.82

0.693

98.64

7.63

2.34

10.38

8364

Architecture

85

22010.85

46.64

7.72

77.1

7.27

4.55

6.21

2811

Geography

91

32010.81

47.33

7.46

81.93

7.25

5.48

9.5

4705

Economics

64

22010.72

30.41

9.35

91.79

7.3

6.38

8.05

2246

Business

81

22010.76

52.69

11.716

95.09

7.38

12.33

4.01

10,234

Law

77

12010.78

54.6

18.877

62.26

6.89

1.68

1.45

3401

Politics

78

12010.85

60.03

19.135

70.1

7.12

2.53

2.37

3005

Social

Work

89

22010.81

65.53

15.987

76.91

7.13

2.67

8.13

3603

Sociology

81

12010.77

66.28

12.639

75.93

7.1

1.25

8.39

1978

Anthropology

84

22010.76

60.76

11.69

67.12

7.18

0.89

6.13

1343

Education

90

22010.84

61.04

13.211

77.69

7.33

4.14

4.46

4148

SportScience

95

42010.83

34.93

6.475

96.57

7.34

6.22

6.71

2502

Scientometrics (2017) 113:735–750 739

123

Table

1continued

Length

Authors

Date

Colon(%

)?(%

)Papers(%

)Word

length

%in

UoA

%outUoA

Number

ofpapers

AreaStudies

82

12010.74

63.96

12.32

56.56

70.2

4974

ModernLanguages

79

12010.84

60.12

7.578

48.21

71.38

2.67

2362

English

Languages

73

12010.9

66.02

5.103

35.73

6.87

0.4

1.17

2469

History

84

12010.81

62.54

8.009

44.01

6.86

0.67

1.09

2822

Classics

66

12010.92

49.75

6.716

29

6.72

01

402

Philosophy

46

12010.96

23.31

9.07

61.67

7.25

0.74

0.45

1334

Theology

75

12010.78

57.37

10.225

37.06

6.82

0.17

1.21

577

Arts

73

12010.86

55.6

4.513

26.39

71.31

3.56

1662

Music

75

12010.95

65.18

6.614

29.63

6.93

0.87

1.74

1255

Media

Studies

79

12010.81

65.72

9.732

52.39

71.35

4.05

1829

Economicssub-groups

Theory

51

22010.76

16.34

4.28

99.61

7.75

6.55

5.09

257

Econometrics

69

22010.72

14.8

3.587

100

7.64

7.85

5.37

223

Theremainder

66

22010.72

34.43

10.815

89.83

7.22

6.16

8.82

1766

Columns(1)mediancharacterlength

oftitle,(2)medianauthornumbers,(3)averagepublicationdate,(4)%

usingacolon,(5)%

usingquestionmark,(6)%

ofsubmissions

whicharejournalpapers,(7)medianword

length,(8)%

ofmultiplepublicationsin

sameUoA,(9)%

ofmultiplepublicationsin

other

UoAs,(10)number

ofjournalpapersin

UoA

whichform

thesample

forouranalysis.Source:

calculatedfrom

dataonjournal

papersin

theREF

740 Scientometrics (2017) 113:735–750

123

for these columns. The number of papers this left for analysis is shown in the final column

of the Table. The median number of authors in journal articles for economics was 2, this is

on the low side with a median across all disciplines of 3. The average was much higher at

11, being pushed up by the very high average number in physics, 92.4. But in general

across the sciences and health disciplines the number of authors tends to be high. The

average can be heavily influenced by one or two outliers. In physics for example, the most

number of authors is 3269, for a paper on the Higgs boson. In the REF, this paper was

submitted 13 times. This is why in this current analysis for the number of authors and also

title length, average word length and the number of citations we work with the median.

That is we calculate, for example, the median title length of all the titles in the sample and

use that instead of the mean title length.

The average date of submission also shows systematic variations across the disciplines.

It tends to be earliest for the sciences and engineering and latest for the arts. Economics is

quite late. This may reflect the ease of writing a new paper in the different disciplines. In

the sciences a grant is often needed and also the team of people tends to be larger than in

the non-sciences. Thus in the non-sciences it may be easier to write a new paper if current

REF output is considered unsatisfactory. However in order for the ease of writing new

papers to explain the timing of submissions, we also need the starting date for research to

be influenced by the REF cycle (Hudson 2016).

The other columns provide us with information on the way economists work in com-

parison with other disciplines. Firstly the proportion of submissions which are journal

papers was quite high at 91.79%. This reflects the importance economists place on journal

papers. Other submissions include books and also working papers. However, the propor-

tion of journal papers is still higher across much of the sciences and engineering. But it

does tend to fall substantially for other disciplines such as law, history and languages. Thus

in classics, less than a third of submissions are journal papers.

There is also information which helps us compare economics with other disciplines in

the structure of the title. Economists tend to be parsimonious in title length relative to other

disciplines, with a median number of characters of 64, only philosophy having a shorter

median title length.7 The longest titles tend to be in the health disciplines and sports

sciences, which is linked to health. Economists are however less parsimonious in their use

of colons. The heaviest users of colons tend to be the arts and social sciences. In sociology

over 66% of submissions feature a colon and in business it was almost 53%. Colons were

least common in mathematics and electrical engineering. The same is true with respect to

question marks. Finally economists tend to use shorter words than the science and health

disciplines and longer ones than the arts and social sciences.8

Hence we can see that different disciplines have different styles and practices with

respect to journal titles, together with the number of authors. The sciences, including the

health sciences, tend to be characterised, for example, by a large number of authors and be

parsimonious in using question marks and colons, even though their titles tend to be

relatively lengthy. This raises the possibility of categorising economics with respect to

other disciplines, at least with extent to title characteristics, the number of authors and the

submission dates for the REF, by the use of cluster analysis. The basic assumption is that

people in contiguous disciplines work in a certain style which tends to characterise those

disciplines. Two disciplines which are close will tend to have similar characteristics in

terms of numbers of authors and the characteristics of the title. Apart from providing

7 This was also the case if we compared averages instead of medians.8 The differences with the social sciences were greater when we used the mean rather than the median.

Scientometrics (2017) 113:735–750 741

123

information on different title characteristics across disciplines, it is also worth noting that

characteristics such as title length, use of colons and question marks have been found to

impact on citations (Haslam et al. 2008; Jamali and Nikzad 2011; Van Wesel et al. 2014;

Hudson 2016).

Grouping disciplines

We use cluster analysis to group disciplines based on title length, number of authors, date

of submission, use of colons and question marks, word length of journal titles and pro-

portion of submissions in the form of journal papers. The basic idea of cluster analysis is to

group, first, the two disciplines that are closest using a Euclidean distance criterion. In

some techniques the squared Euclidean distance is used. The Euclidean distance is formed

by finding the difference in the values of the first variable (e.g., title length) between two

disciplines and squaring the result, repeating for each variable, adding the all the squared

differences together and taking the square root of this. Out of all the disciplines in the

study, the pair that are closest join first and lowest in the dendrogram (see Fig. 1). When

groups are being joined together, some measure of the collection of values of a variable is

needed and various possibilities are discussed later.

We consider only journal articles, and not other publications such as books which may

in themselves follow a different format in their title than do journal papers. The per-

centages of joint papers shown in the penultimate two columns of Table 1 were not

included in the cluster analysis as they will tend to reflect the number of authors, and hence

this would be to an extent double counting this characteristic. In addition, the number

submitted outside the discipline (the penultimate column of the table) partly depend upon

how the boundaries are drawn by the REF itself, rather than inherent characteristics of the

01

23

45

L2 d

issi

mila

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mea

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Clin

ical

Med

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ePu

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Hea

lthAl

lied

Hea

lthAg

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ture

Biol

ogic

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ycho

logy

Earth

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naut

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Eng

Gen

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Elec

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Civ

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Phys

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Soci

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Fig. 1 Dendrogram based on average linkage

742 Scientometrics (2017) 113:735–750

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discipline. We standardised the data by transforming each variable into a Z variable. This

is frequently done when the different variables are substantially different (Everitt et al.

2011). This was done in STATA and the algorithms used to calculate the different clusters

using different assumptions were also those which are given in STATA. Figure 1 shows

the dendrogram calculated using the method of average linkage. As its name suggests, this

uses the average over all papers in a cluster for each variable as the measure of cluster

location. The average linkage method tends to work well in many situations and is rea-

sonably robust (Kaufman and Rousseeuw 1990). The diagram shows the Euclidean dis-

tance. Two subject matter areas that have highly similar characteristics join low in the

diagram, hence aeronautical and general engineering are highly similar. At higher values of

the vertical axis, clustered pairs are joined by a third area, or group of areas, with largely

similar characteristics to the pair. Other areas that have somewhat similar characteristics

are also paired. Moving up the diagram, the number of clusters reduces and each contains

more heterogeneous, although somewhat similar, areas.

Economics is initially grouped with philosophy, although quite late in the clustering

process, hence the linkage is not that strong. At the same approximate level of dissimilarity

is a second group involving architecture, geography, sports science, business, social work,

education, sociology, anthropology, area studies, law and politics. These correspond clo-

sely to the social science disciplines and REF panel C. This group is subsequently linked to

a second grouping comprising modern languages, media studies, history, theology, English

language, music, the arts and classics, which largely correspond to the arts and humanities

and REF panel D. It is these two combined groups that economics and philosophy even-

tually are linked. There is then one further large grouping. This is made up of sub

groupings of allied health, agriculture, biological sciences, psychology and earth systems,

which largely come from Panel A, the health sciences, and the four engineering disciplines

which come from panel B, the sciences. These two sub groups merge and are then joined

by chemistry, physics, a sub group of maths and computer science, and finally clinical

medicine and public health. To a large extent the clusterings are intuitively plausible. But

there are some surprises. For example, the clustering of sports science with architecture

and geography. However this does take place quite late which suggests that the linkage

with sports sciences and the other two disciplines is not that strong. Even so there are

plausible connections, with geography forming a bridge between the other two. The full

title of sports science is sport and exercise sciences, leisure and tourism, and the latter two

areas make the linkage with geography plausible. Similarly with geography and the last

two elements of architecture, built environment and planning. But the linking of psy-

chology, psychiatry and neuroscience with earth systems and environmental sciences is

more difficult to rationalise. However the subsequent linking of this subgroup with allied

health, biological sciences and agriculture, veterinary and food science is easier to ratio-

nalise, e.g. the link of earth systems with agriculture and biological sciences.

Hence we can see that groupings based on journal paper characteristics categorise the

different disciplines into the sciences, social sciences and the arts in a manner which

largely supports the conventional groupings, as can be seen from Table 2, where these

clusters are listed. We choose seven clusters9 as this allows the main clusters to emerge,

given that some UoAs such as clinical medicine will tend to be on their own, not linking to

other UoAs. In this case both the social sciences and the arts and humanities are well

identified. Most of the sciences in both panels A and B form a single cluster, with the

exceptions being clinical medicine, public health, chemistry and physics. Mathematics and

9 Corresponding to approximately 2.6 units of Euclidean distance on the dendrogram shown in Fig. 1.

Scientometrics (2017) 113:735–750 743

123

Table 2 The panels, and UoAs in the 2014 UK REF

Unit of assessment (UoA) Clusters

A B C D E

Panel A Health Sciences

1 Clinical Medicine 1 1 5 4 1

2 Public Health, Health Services & Primary Care 2 1 2 4 4

3 Allied Health Professions, Dentistry, Nursing & Pharmacy 3 1 6 4 1

4 Psychology, Psychiatry & Neuroscience 3 1 6 5 1

5 Biological Sciences 3 1 6 5 1

6 Agriculture, Veterinary & Food Science 3 1 6 4 1

Panel B Sciences

7 Earth Systems & Environmental Sciences 3 1 6 5 1

8 Chemistry 3 2 6 5 1

9 Physics 3 2 4 5 2

12 Aeronautical, Mechanical, Chemical & Manufacturing Engineering 3 2 6 5 1

13 Electrical & Electronic Engineering, Metallurgy & Materials 3 2 6 5 1

14 Civil & Construction Engineering 3 2 6 5 1

15 General Engineering 3 2 6 5 1

10 Mathematical Sciences 4 3 6 6 3

11 Computer Science & Informatics 4 3 6 6 3

Panel C Social Sciences

16 Architecture, Built Environment & Planning 5 5 7 7 5

17 Geography, Environmental Studies & Archaeology 5 4 7 7 5

19 Business & Management Studies 5 4 7 7 5

20 Law 5 4 7 2 5

21 Politics & International Studies 5 4 7 2 5

22 Social Work & Social Policy 5 4 7 2 5

23 Sociology 5 4 7 2 5

24 Anthropology & Development Studies 5 4 7 2 5

25 Education 5 4 7 2 5

26 Sport & Exercise Sciences, Leisure & Tourism 5 5 7 7 5

18 Economics & Econometrics 7 6 3 1 7

Panel D Arts & Humanities

27 Area Studies 5 4 7 2 5

28 Modern Languages & Linguistics 6 7 7 3 6

29 English Language & Literature 6 7 7 3 6

30 History 6 7 7 3 6

31 Classics 6 7 7 3 6

33 Theology & Religious Studies 6 7 7 3 6

34 Art and Design: History, Practice & Theory 6 7 7 3 6

35 Music, Drama, Dance & Performing Arts 6 7 7 3 6

36 Communication, Cultural & Media Studies, Library & Information Management 6 7 7 3 6

32 Philosophy 7 6 1 1 7

Cluster A fitted by average linkage; B, ward’s linkage; C, single linkage; D complete linkage. E is fitted byaverage linkage excluding the number of authors

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computer science also form their own sub-group. Economics does not fit in well with the

other social sciences, nor indeed does it fit in with the sciences. It fits best with philosophy,

although the fit is not that strong and if we move to nine clusters it is on its own, not linked

with philosophy. If we use other methods10 to identify the clusters, the results viz a viz

economics change relatively little as can also be seen from Table 2. For example, when

using Ward’s method, there is a clearer split identifying panels A and B, and the social

sciences form two clusters—which join together again when we reduce the number of

clusters to 6. The dendrograms give more insight than the simple identification of clusters,

but what this Table also reveals is that while the contents of a cluster might be identical or

almost identical under different linkage assumptions, the ordering of clusters depends on

the algorithm and other assumptions. The final column shows the clusters using average

linkage when we omit the number of authors. The results are similar to before, although

social sciences now has two sub clusters and in panel B, chemistry and physics are now

part of the other sciences, but with maths and computing excluded. But still the clusters are

based around the different REF panels and hence these results are not being driven by large

differences in the number of authors between disciplines. The same is true if we omit the

proportion of papers which are journal papers.11

01

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Fig. 2 Dendrogram based on average linkage with sub-groups for economics

10 Ward’s method is based on increasing the overall within-cluster variance to the smallest possible degree.Single linkage is based on the minimum distance and complete linkage is based on the maximum distance.11 If we repeat the analysis but using the raw data, not the standardised one, then there are some changes.The clusters still identify the different panels well, but now economics is weakly linked with mathematicsand computer science. Again there is no evidence of a linkage with either the rest of the sciences or thesocial sciences.

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Splitting up economics

In this section we divide the economics papers into three groups, those published in theory

journals, those published in econometrics journals and all the remaining papers. We used

the same categorisation principle as Hudson (2013), i.e. a journal was counted as a theory

journal if some version of the word ‘theory’ appeared in the title and similarly for

econometrics with respect to variations on ‘econometrics’. For this exercise, the journal

Econometric Theory was counted as an econometrics journal. Table 1 shows the summary

data on these three sub groups of economics. There are substantial differences, with titles

of both theory and econometrics journals having significantly, at the 1% level, lower

proportions of papers with both colons and question marks. The titles of papers in theory

journals are exceptionally parsimonious, whilst their average word length is high relative to

other disciplines. Although not as high as papers in theory journals, word length in

econometrics journals also tends to be high relative to other disciplines. The penultimate

column shows that a submission which spans disciplines is relatively uncommon for papers

in theory and econometrics journals, but slightly more common in the rest of economics.

Redoing the cluster analysis in a manner consistent with that shown in Fig. 1 results in

the dendrogram shown in Fig. 2.12 Papers published in econometrics journals are linked in

with maths and computing as subsequently are papers published in theory journals, they

thence join with the sciences, although at a high level of dissimilarity. The rest of eco-

nomics now links with business and thence to the social sciences. This result does not

change if we use the other methods for clustering. The clustering algorithm is a hierar-

chical one and expanding the disciplines has had only limited repercussions on the

structure of the other clusters, and this involves mainly business and philosophy. The close

tie of econometrics to mathematics is not that surprising, although that with computer

science a little more so. Perhaps the most surprising conclusion is that ‘‘other economics’’

ties with business and follows a completely different path up the dendrogram to that of

theory and econometrics. It is this second part which is linked with the social sciences.

These differences suggest that economics is a broad discipline compared with some others,

although it is also a discipline with a degree of heterogeneity. It is also not surprising that

these clusters are different to those that we get using citation analysis (Rafols et al. 2010).

The latter will relate primarily to subject matter rather than to academic style of working.

The REF data provides more information on the linkages between economics and other

subjects via the submission to other UoAs of papers also submitted to the economics UoA.

The proportion of joint submissions, shown in the penultimate column of Table 1, for the

theory and econometrics journals combined is significantly lower, at the 1% level, than for

the rest of economics.13 Ignoring Business UoA ‘partners’, for theory and econometrics

seven out of twelve ‘partners’ were in mathematics or computer science. For the rest of

economics the spread was wider. Out of 71 non-business UoA ‘partners’, nine were in the

mathematics or computing UoAs, with the other largest groupings being public health (11),

history (10), social work and social policy (8) and education (7). These linkages will

partially reflect closeness of subject matter, as in citations, but also possibly closeness in

working styles.14 The results, albeit with small samples, again suggest that the more

12 For this we assume that the proportion of papers published in journals is the same as for the discipline asa whole, for all three parts of the discipline.13 The majority of these were with the Business UoA. This may not reflect economists working with non-economists as many economists are in business schools.14 Although it is also possible these papers involve people with differing skills.

746 Scientometrics (2017) 113:735–750

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quantitative part of economics is closely linked with mathematics and computing, but that

the rest of the discipline is spread more widely across parts of the sciences, the social

sciences and also the arts.

Conclusions

This paper represents one of the first attempts to identify, using quantitative analysis, the

linkages between different disciplines based on the stylistic characteristics of journal

papers. We are able to identify subjects, as defined in the REF, into clusters which closely

resemble those which form the four main REF panels and also more generally what are

thought of as the sciences, the social sciences and the arts and humanities. This indicates

both that contiguous disciplines have similar style characteristics evident in the title of the

journal papers in those disciplines, and that using these we are able to link the different

disciplines together in a manner which broadly is reflective of the social sciences, sciences

and arts and humanities as they are generally understood. Thus linking cluster 3 to panels

A and B, cluster 5 to Panel C and cluster 6 to Panel D as in column A of Table 2, correctly

identifies 29 of the 36 disciplines. The disciplines of physics and public health however,

are difficult to categorise and maths and computer science form a separate group from the

rest of the sciences. Economics as a whole is linked with philosophy, although not strongly

so. Only area studies is allocated to a ‘wrong’ panel. The fact that we have been able to

successfully allocate disciplines to clusters which so closely resemble the REF panel

groupings suggests that our methodology is sound.

On the basis of our analysis, it seems possible that most of economics does not closely

link with any of the other disciplines. Economics tends to have relatively few authors, but

more than the arts and humanities, tends to be parsimonious in title length and to an extent,

particularly when compared with the other social sciences and the arts and humanities, in

the use of colons. When we look at sub-disciplines within economics, we find that

econometrics first links with maths and these are then joined by economic theory and

computer science. Business and the rest of economics link together and thence with the rest

of the social sciences, although at a relatively high level of dissimilarity and hence the

linkage is not that strong. However there is little evidence of a link with the sciences per se.

This is not unexpected, large grants involving several labs have become increasingly

important in the sciences, at least as represented by the leading papers (Galison and Hevly

1992). This may have tended to increase author numbers and this too will leave a footprint

on title styles (Hudson 2016). Of course grants are important in economics too, but not to

the same extent as in big science.

With respect to the question posed in the title of this paper, our view, in part informed

by these results, is that the subject matter of economics places it clearly as a social science,

but many in the discipline act as if it were a science. Economics has gradually developed

into an interdisciplinary subject that uses scientific methods, for example setting out and

formally testing hypotheses, and particularly a mathematical approach, to solve social

science problems. This dual aspect may explain why it fails to fit neatly with either as in

fact it combines elements of both. It is a social science pursued with more quantitative

rigour than much of the rest of the social sciences. This emphasis on technique then pushes

economics and economists away from the other social sciences and in some sub-disciplines

towards maths. Placing economics as a science, emphasises technique above subject

matter. But there is then a danger that technique becomes an end in its own right, rather

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than a means to an end, i.e. a vehicle which allows a more refined analysis of economic

issues which have wider relevance. Often, for example when developing a new econo-

metric technique which subsequently becomes widely used, this is justified. But sometimes

this is not the case, which helps explain the relative isolation of the discipline as noted

earlier. This does not mean that economists should abandon their quantitative focus, but

that they should always strive to ensure that their work contributes, directly or indirectly, to

an understanding of a real world issue. This is consistent with Frey’s (1999) observation

that economics is part of those sciences which deal with actual problems of society, but

that most economists attempt to imitate the sciences and that economics can be regarded as

a branch of applied mathematics. It is also consistent with the view that that economics,

particularly neoclassical economics, is the most mathematical of the social sciences (Porter

1996).

There are two further implications of this, both of which might be explored in further

work. Firstly, modern universities have mainly organised their structure along the ‘tree of

knowledge’ type model (Lenoir 1997), according to which knowledge is split into branches

(for example, sciences, social sciences), then into major disciplines and thence into sub-

disciplines and specialties. This has tended to result in economics being linked with the

social sciences or business schools. Seldom are they linked with any of the sciences. This

may lead to problems in being involved with administrators and academics who do not

really understand the way economists work, nor their values. Secondly, the ethos econo-

mists perceive themselves as being part of tends to set the confines within which successful

economists must work. If economists perceive themselves as mathematicians, then this sets

out the theoretical style we see in many journals. If they see themselves as scientists then

the emphasis moves to hypothesis testing (Mayer 1980). If, on the other hand, they see

themselves as social scientists this will focus attention more on policy outcomes and a

greater readiness to use the results of the other social science disciplines.

The analysis has focused on papers submitted to the REF. Whilst this provides a large

number of papers to analyse, they are biased towards the better, higher quality papers.

Further analysis could extend the data set to cover a more random set of papers across a

range of journals in each discipline in order to determine whether the writing character-

istics of these high quality papers translate to the discipline as a whole. In addition, further

work could estimate clusters based on other aspects of papers, such as length and the

typical structure of a paper. Abstracts too differ between disciplines. In many journals, it is

a single paragraph of between 100 and 250 words. But in some of the science disciplines

the abstract too is divided into sections. Other possible characteristics include the numbers

of equations, footnotes or endnotes, quotations and use of explanatory diagrams or flow

charts, use of regression analysis and the ratio of the length of the concluding section to the

rest of the paper.15 Finally, we have divided the subject along into theory, econometric and

other areas. This could have been taken further, dividing by subject subfields, such as

health economics, labour economics and macroeconomics, and then studying these sub-

field’s attribution by means of semantic similarity. Defining the subfields could be done,

15 For example an examination of the first issues of 2016 for several journals revealed substantial differ-ences in the use of equations. In the social sciences they were relatively rare. Unsurprisingly, in mathematicsusage was very high, on occasion over 100. In core theory (Journal of Economic Theory) and econometrics(Econometrica) journals the average was approaching that of mathematics—although the latter had muchmore mathematical content. The number of equations in three core mainstream economics journals(American Economic Review, Quarterly Journal of Economics and Journal of Political Economy) was muchless, although still far in excess of the rest of most of the social sciences. Details available from author.

748 Scientometrics (2017) 113:735–750

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e.g., by looking at title papers in journals recognised as representing the different

disciplines.

Acknowledgements I would like to acknowledge the comments of two anonymous referees which havesubstantially influenced and improved the paper.

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 Inter-national License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution,and reproduction in any medium, provided you give appropriate credit to the original author(s) and thesource, provide a link to the Creative Commons license, and indicate if changes were made.

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